Bachelors thesis in Economics

Mälardalens University

Supervisor: Johan Lindén

30th

of May 2014

Start-ups and education

– How does the proportion of inhabitants with tertiary education affect

the number of start-ups in Swedish municipalities?

Anna Kain Wyatt

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Abstract

There are many different factors affecting the number of start-up firms in Swedish municipalities. This

paper focuses on the relationship between the proportion with tertiary education and the number of start-

ups. The purpose of the paper is to investigate which factors influence the number of start-ups with

focus on the educational variables. The variables have been chosen with help from earlier studies and

theories regarding economic growth and start-ups. With data from SCB and Tillväxtanalys the

relationships between these variables and the number of start-ups has been analysed. The relationship

will be studied in both a quantitative part and a qualitative part. In the qualitative part it can be seen that

the municipalities with a higher proportion of start-ups differ from those with a lower proportion of

start-ups per capita. The quantitative part is done through regression analysis that show which variables

have a significant impact on the number of start-ups in the years 2007-2010. The results of the

regression analysis show that several variables have a significant effect on the number of start-ups. The

variables are: tertiary education, whether the municipality has a university or is adjacent to a

municipality with a university, population growth, population density and unemployment.

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Table of contents

1 Introduction ......................................................................................................................................... 4

1.1. Background .................................................................................................................................. 4 1.1.1 What challenges are Swedish municipalities facing? ........................................................... 5

1.2. Research question ......................................................................................................................... 5 1.3. Purpose ......................................................................................................................................... 6 1.4. Structure of the study ................................................................................................................... 6

2. Earlier studies ...................................................................................................................................... 6 2.2. Summary of earlier studies ........................................................................................................... 9

3 Theory ............................................................................................................................................... 10 3.1. Human capital............................................................................................................................. 10

3.2. Growth theory............................................................................................................................. 10 3.2.1. Exogenous growth model.................................................................................................... 10 3.2.2. Endogenous growth models ................................................................................................ 13 3.2.3. Company start-ups .............................................................................................................. 15

4. Empirical Study................................................................................................................................. 19 4.1. Review of variables .................................................................................................................... 19

4.1.1. Dependent variable ............................................................................................................. 19 4.1.2. Independent variables ......................................................................................................... 19

4.2. Qualitative .................................................................................................................................. 21 4.2.1. Unemployment .................................................................................................................... 23

4.2.2. Population growth ............................................................................................................... 23 4.2.3. A university in the own or neighbouring municipality ....................................................... 24

4.2.4. Tertiary Education............................................................................................................... 24 4.3. Quantitative part ......................................................................................................................... 26

4.3.1. Explanation of variables...................................................................................................... 26 4.4. Variables ..................................................................................................................................... 27 4.5. Description of data ..................................................................................................................... 27

5. Results ............................................................................................................................................... 27 5.1. Regression models ...................................................................................................................... 27 5.2. Results ........................................................................................................................................ 28

6. Analysis ............................................................................................................................................. 29 6.1. Modell 1 ..................................................................................................................................... 29

6.2. Modell 2 ..................................................................................................................................... 29 6.3. Modell 3 ..................................................................................................................................... 30

6.4. Modell 4 ..................................................................................................................................... 30 6.5. Educational variables ................................................................................................................. 30 6.6. Control variabels ........................................................................................................................ 31

7. Summary ........................................................................................................................................... 32 8. References ......................................................................................................................................... 34

Appendix ................................................................................................................................................... 36

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1 Introduction

1.1. Background

The economic growth in Sweden is dependent on what happens on a regional and local level around the

country. The total growth is a sum of the growth and the development that takes place in the

municipalities. With this as a starting point this paper will investigate the relationship between the

proportion of highly educated inhabitants and the number of start-ups in that same municipality.

Since more than a decade ago, the regional development policy in Sweden is built so that the main

responsibility for regional development has moved from the state to every region. Earlier this was both

planned and done by the state. The purpose of the regional growth programs is that representatives from

the region, the labour market and the non-profit sector (NGO:s) shall develop plans and policies for the

regional growth together. (Brulin m.fl 2009)

The European Union’s structural funds are a form of regional support that aims to support growth in the

countries regions. The Swedish Riksdag’s EU-upplysning describes the support like this:

“EU:s regional support aid to Sweden shall help create new businesses and jobs. Business

organisations, municipalities, local organisations and associations for example can seek support for

projects.” (EU-upplysningen, 3 January 2013)

The government agency that is responsible for distributing the regional support from EU is

Tillväxtverket. Tillväxtverket stresses that a condition for regional economic growth in Sweden is more

start-ups and growing firms. The most important factor for creating long term economic growth is,

according to Tillväxtverket, new firms that create new jobs. With this in mind we can conclude that a

high unemployment rate is a threat to long term economic growth. The solutions that Tillväxtverket

suggests are promoting entrepreneurship in the country in conjunction with local initiatives.

(Tillväxtverket, 25 December 2012) This paper will focus on how the proportion of highly educated

inhabitants, can contribute to a positive economic development through starting firms.

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1.1.1 What challenges are Swedish municipalities facing?

Demographic changes are occurring concerning age and age distribution In Sweden, and this will affect

the municipalities. People live longer and a larger share of the population will be elders. More focus

must lie on actions concerning elders as this affects for example health care,

This also lead to the fact that as the share of the population in working age will decrease; less people

will work with production of goods and services and therefore tax incomes for the municipalities will

decrease. The share of the population supporting children and elderly is decreasing. (SKL, 3 January

2013)

Together with the changes in age distribution there is also a problem of negative population growth in

many of the Swedish municipalities. In 2007 131 of 290 municipalities had negative population growth,

and in 2010 139 had negative population growth (SCB). To have a negative population growth is

negative from an economic point of view, since for example the tax incomes decreases. This leads to

that the municipalities have a decreased ability to offer the community services they are responsible for.

(Tillväxtverket, 25 December 2012)

Many of the theories about regional development focus on cluster formation, with a tight collaboration

between firms and supporting organizations such as educational institutions. A challenge for Swedish

municipalities is to create good conditions for business. If it is easy to start up a company this opens up

for more people doing this. (Tillväxtverket, 26 January 2012)

With this as a background it can be concluded that it is important to study what factors that have a

positive effect on regional development.

1.2. Research question

Do a higher proportion of university graduates lead to an increased number of start-ups?

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1.3. Purpose

With starting point in the background above the purpose of this paper is to investigate if an increased

share of tertiary educated will lead to an increase in the number of start-ups per capita. The thesis is that

the more firms is started, the larger share of the municipality’s inhabitants that has a university degree.

This is of importance for the economic development of the region.

1.4. Structure of the study

Chapter two goes through earlier studies made about education, start-ups and regional growth. In

chapter three the theoretical framework for the study will be presented. Chapter four is the empirical

study and consists of two different parts. A quantitative part where municipalities with a low share of

start-ups per capita is compared with those that have a high share are compared and a qualitative part

where a regression analysis is made. In chapter five the results from the regression analysis is presented

and in chapter six they will be analysed. Chapter seven consist of a summary of this study.

2. Earlier studies

“Does human capital matter for growth in OECD countries?”

In their study Bassani and Scarpetta investigates the relationship between human capital and economic

growth. They investigate the subject through an empiric cross-country study of OECD countries in

between 1978 and 1998. One of the variables used to measure human capital is education. Earlier

studies have been inconsistent regarding this matter and several has shown a negative relationship

between human capital and growth. (Bassani & Scarpetta 2001)

The study’s theoretical discussion is based on an augmented neoclassical growth model, where human

capital is added as a factor of production. Human capital also assumes to have a constant return to

scales. The augmented neoclassical model has very specific predictions for the long run parameters of

human capital, physical capital and population growth.

But the authors conclude that the results that are found rather points to a consistency with endogenous

growth models. (Bassani & Scarpetta 2001)

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“(…) these results are not consistent with the human-capital-augmented version of the Solow model, but rather

they might support an endogenous growth model à la Uzawa-Lucas, with constant returns to scale to “broad”

(human and physical) capital.” (Bassani & Scarpetta 2001)

The results of the study show that human capital has a significant and positive impact on output per

capita growth. Also the individual benefits from tertiary education, more education pays off and the

returns are higher the more years you study. Regarding convergence to the steady state they found out

that the countries were in fact moving faster towards steady state than predicted in the model. (Bassani

& Scarpetta 2001)

”Nyföretagande i Sverige”

The survey Nyföretagande in Sweden discusses factors that influence the number of start-ups. According

to the authors, researchers have identified 41 factors that affect the proportion of start-ups in different

ways. There are many different kinds of factors such as attitudes, taxes, regulations about employment,

population, labour market and the size of the public sector relative to the private.

One of the factors that is brought up is level of education. The authors has found out that earlier studies

has come to different conclusions regarding how educational levels affect the number of start-ups. Some

studies shows a significant relationship between educational levels and the number of start-ups while

others concludes that there is no relationship. Among those studies that shows a significant relationship

there is both those that find a negative relationship and those that finds a positive one (Calidoni et al.,

2007)

The authors conclude that even as there many different factors influence entrepreneurship and start-ups

no single study has managed to show what actions are needed to reach the goal. The answer seems to lie

in a complex interaction between surroundings, the firms and individuals. (Calidoni et al., 2007)

”Nyföretagande, näringslivsdynamik och tillväxt i den nya världsekonomin.”

In Nyföretagande, näringslivsdynamik och tillväxt Karlsson and Nyström identify some conditions for

start-ups that are possible to influence through politics. (Karlsson & Nyström 2007)

One basic condition for incentives to start up a firm in an area is however there is any local demand, if

there is a market for the product that you indent to sell. If there is no market for the product there is no

idea to start a company producing it. According to Karlsson & Nyström earlier studies has investigated

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if there is a relationship between increased population growth and higher demand. Studies have had

different results, so there is no clear relationship between increased population growth and demand.

Neither has the earlier studies showed any relationship between an increased income per capita and the

number of start-ups. (Karlsson & Nyström, 2007)

Investments in education and research have not had the expected positive effect on the economic

growth. The authors describe that there are difficulties for new firms to gain access to research based

knowledge because since they are new they do not have any natural ways of connecting to research

institutes. Firms that have been active for a longer time have had plenty of time to establish contact with

research institutes or have been doing their own research. (Karlsson & Nyström, 2007)

Another way for firms to benefit from research-based knowledge is by being in regions where research

is conducted. With much import and where direct investment is made, this is often in the metropolitan

regions. The authors also stress that the local availability of a highly educated workforce is a major

factor in the proportion of start-ups increases. (Karlsson & Nyström 2007)

”Kartläggning av regionalt nyföretagande ”

In their thesis Steinmertz and Åkesson brings up both structural and individual factors that influence the

number of start-ups. Here there will only be focus on the structural aspects that they bring forward.

Some of the factors mentioned are: the labour market, education, income, population structure and

industry structure. They also bring up cultural differences as a reason to why there are differences in the

proportion of start-ups between different regions. (Steinmertz & Åkesson 2006)

Their thesis has through interviews investigated incentives for starting firms in Norrbotten. The authors

conclude that the interviewed people had reasons such as personal fulfilment and a desire to carry out a

mission rather than purely economic reasons. About half of the respondents also indicated that the threat

of unemployment was a contributing factor to the creation of their firms.

Based on the fact that the respondents operated in Norrbotten, the authors emphasize the importance of

analysing different region’s specific conditions in order to understand the motives for entrepreneurship.

(Steinmertz & Åkesson 2006)

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”Tillväxtens drivkrafter”

The report Tillväxtens drivkrafter studies which different factors influence economic growth on

national, regional and company level.

The authors state that the most important factors for long-term economic growth are technological

development and innovation. The development of this occurs primarily internally in firms but also

benefits from investment in the public education system. (Wigren et al., 2005)

The public should also focus on efforts to facilitate the exchange of knowledge and learning between

firms as well as between firms and other actors in society. This with a special focus on universities and

colleges. (Wigren et al., 2005)

The authors emphasize start-ups as an important step on the way to economic growth that promotes new

knowledge. This is because new and small businesses are often more flexible and quicker at decision

making than their larger counterparts. If small firms cooperate there is a high chance for a good long-

term development. (Wigren et al., 2005)

Another conclusion drawn in the report is that knowledge and learning are developed in a system of

interacting actors; it is vital that businesses and other stakeholders are willing and have the capacity to

disseminate knowledge and innovations. (Wigren et al., 2005)

2.2. Summary of earlier studies

To sum up, there are a number of previous studies have shown what affects the number of start-ups.

Most studies mention education and dissemination of knowledge as important aspects that have a

significant impact on the number of start-ups. This study will focus on the impact of formal education.

Earlier studies have not shown any consistent results regarding the impact of higher education on the

number of start-ups. Some studies point out that the access to labour with a university degree is an

important factor for a higher number of start-ups. Other studies show a negative relationship and that

investments in education hasn’t led to the expected results.

Other important factors that have been mentioned in earlier studies are: industry composition,

demographics, income, population growth and labour market. These will serve as control variables in

this paper.

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3 Theory

Researchers have no straight answer to which specific factors influence the amount of business startups

in a region. This section will look into different theories concerning start-ups of new businesses and how

these can be related to education and knowledge. The theoretical discussion will focus on human capital

and its influence on economic growth and start-ups.

3.1. Human capital Through education we create knowledge that enables the individual to increase their productivity. In

1964 Becker created economic models regarding human capital and their positive output. In the models

below we can see that education and knowledge has a positive effect on output and GDP. In his human

capital model Becker shows that investments in education have a positive rate of return in terms of

income for the individual. (Becker 1964)

Becker concludes that there are many different ways to invest in human capital: “schooling, on-the-job

training, medical care, migration, and searching for information about prices and incomes” (Becker

1964 p11) This study will focus on the human capital accumulated from formal education.

Through Becker’s work we can see that there is a positive outcome of higher education both for the

individual, the firm that hires and the economy as a whole. With this in close memory we will now look

at economic growth theory and how human capital affects economic growth. We will also see how a

supply of skilled labour can be a contributing factor for firms thinking about establishing in a

municipality.

3.2. Growth theory With help from growth models we can analyse what factors that affects economic growth. This part will

consist of both exogenous and endogenous growth models since they both can include human capital but

with different approaches and basic assumptions underlying the analysis.

3.2.1. Exogenous growth model

Solow growth model

The Solow growth model is a long term growth model consisting of two factors of production, capital

(K) and labor (L). With different combinations of these two factors the economy reaches different levels

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of GDP (Y). The model assumes that the two factors have a positive but diminishing marginal product;

investments in the factors will generate smaller and smaller returns. (Carlin & Soskice, 2006)

Savings are decided outside the model and this is the reason the model is exogenous, you can’t affect the

savings and therefore not the level of investment inside the model. The model also assumes that there is

a constant relationship between savings (S) and investments (I).

The model is based on two different parts, the aggregated production function:

(1)

And capital per capita changes over time

– – (2)

Where (k) is capital per capita, (s) share of GDP per capita, (δ) depreciation rate of capital and (n) is

population growth. Since the savings (s) is equal to the investments (i) the capital stock is influenced by

changes in savings per capita. The depreciation rate (δ) tells us how quick the capital wear out and the

population growth (n) influences how much capital there will be per labour unit. If population grows

faster than the capital stock this will change the relation between the variables (K) and (L) and therefor

(Y). (Carlin & Soskice, 2006)

With help from the variables in this function we can see what affects the long term economic growth. A

country should strive to be in the steady state (k*), this is the point where the relation between capita (K)

and labour (L) remains constant, where the change in capital per labour unit is zero (Δk=0)

When the economy is exposed to a shock, which could for example be higher population growth the

economy moves away from steady state towards a new level. (Carlin & Soskice, 2006)

Convergence

The Solow model assumes that a country has a long term steady state (k*). When a country for some

reason moves away from their steady state, this will affect the speed of their development.

Two countries with the same level of steady state will develop at different speeds depending on their

starting point. The country that is furthest from the steady state level will develop at a faster speed than

the one closer. (Carlin & Soskice, 2006)

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Two countries with different steady states will not converge and will stay at different levels until one of

them is affected by a chock that might adjust the steady state towards the other country. In this case the

cause of the differences in development speed can be that the countries are at different distance from

their own steady state. The country further away from its own steady state will experience a faster

growth, while the country closer to its own steady state will experience a slower growth. (Eliasson &

Westerlund 2003)

Technological changes affect the steady state and can therefore change the economic relations between

different countries. When it comes to different regions within a country they often have access to the

same technology, which makes it possible to look at other factors that could affect the differences within

a country. (Eliasson & Westerlund 2003)

Solow Swan with technological change

In reality a lot more things are present in the economy than capital and labor, and one of the important

things is technological change. When technology changes the conditions for production changes; new

inventions can change the ways of production through for example new machinery. I will not go through

this extended Solow Swan model so much but the big difference compared to the basic model is that

through technological change the output per worker is allowed to grow. Earlier you had to hire more

workers to get more done, but now their efficiency can be affected by technological changes.

Instead we will focus on what difference is made when the model allows for both technological change

and human capital. (Carlin & Soskice, 2006)

Solow model with technology and human capital

In their empiric study Mankiw, Romer and Weil show how it is possible to include human capital in the

Solow model and still keep it exogenous. They find that the accumulation of human capital

correlateswith savings and population growth. (Mankiw et al., 1992)

They present this production function where human capital is included:

(3)

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Where (H) stand for human capital and (A) for technological change. In the Solow model savings equals

investments, in this augmented model the savings are split into two parts, the part invested in physical

capital (sk) and the part invested in human capital (sh). (Mankiw et al., 1992)

If:

(4)

Then we can analyse the change in capital and human capital per effective unit of labour:

(5)

(6)

Changes in capital per effective unit of labour is dependent on the amount of savings invested in capital,

while changes in human capital per effective unit of labour is dependent on the amount of savings

invested in human capital. The model assumes that consumption, physical capital and human capital all

have the same production function, in other words, that it is possible to change between them at no cost.

Human capital depreciates at the same rate as physical capital. (Mankiw et al,. 1992)

Through their model Mankiw et al shows that the income per capita depends on population growth and

accumulation of human and physical capital (Mankiw et al., 1992). This is important to notice for this

study, since the thesis is that tertiary education should have a positive impact on economic development

and growth.

3.2.2. Endogenous growth models

Endogenous growth models give us the possibility to overcome diminishing returns to capital. If we

include knowledge and human capital in an endogenous growth model we can move away from

diminishing returns. Investments in human capital will not have a diminishing return which makes it

possible to affect the level of the steady state through investments in for example education. (Carlin &

Soskice, 2006)

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Robert Lucas has introduced human capital in to an endogenous growth model; trough this equation he

shows the accumulation of human capital:

(7)

Where (ht) is the amount of human capital per person available to the economy and (1-ut) is the fraction

of labour hours spent in education to learn new skills and (c) is a scaling parameter. The equation shows

us the relation between the amount of human capital and the time spent gaining this knowledge.

Together with Paul Romer’s model we can see how this affects the levels of production. (Carlin &

Soskice, 2006)

Accumulation of ideas

Paul Romer (1990) has developed a model that shows us the accumulation of ideas. He uses a

modification of the Solow Swan model where technology is endogenous. Through this model he can

show how accumulation of ideas affects the economic growth.

Accumulation of ideas:

(8)

stands for the change in the amount of ideas, At stands for the current stock of ideas. LtR stands for the

number of workers employed in R&D and c is a constant. If η = 1there is constant returns to the

accumulation of ideas. This means that the size of η is decisive for how the accumulation of ideas

develops. (Carlin & Soskice, 2006)

If we turn to the Cobb-Douglas production function for final goods we can see how the accumulation of

ideas affects the level of productivity. One difference compared to earlier growth models is that we

distinguish between workers employed in manufacturing and those employed in research (Lt=LtY+Lt

R).

How accumulation of ideas affect the level of productivity:

Yt = Ktα (AtLt

Y)1-α

(9)

Yt stands for the level of productivity, Kt is capital and LtY is the part of the labour employed in the

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manufacturing sector. Here we can see that At is an endogenous factor that affects the level of

productivity together with capital and labour. (Carlin & Soskice, 2006)

When At grows in both models above this means the amount of ideas increase. As we can see this is

important for the economic growth in a country. According to Romer the amount of ideas has to grow

over time to create long term economic growth. In model one above we can see that this happens if the

number of researchers is increasing relative to the remaining population over time. To achieve this the

government needs to affect the technological development through investing in research and education.

This will in the long run affect the economic development. (Hellström, 2012)

Even though the models above concern the effects of education and research within a company it is

useful for the question of this paper that, they show that knowledge is an important part of long term

economic growth. And through this we learn something useful: according to these models knowledge

doesn’t have diminishing returns. The more you invest in education and knowledge the more you get

back.

3.2.3. Company start-ups

There are a lots of different theories regarding firms and what different factors that influence the amount

of start-ups. Within them we can see two main focuses: one type of theories focus on the surroundings

and regional economic conditions while the other type focus on the individual and the individuals

qualities. In this paper the focus will be on the surroundings and on what the local governments can do

to increase the proportion of start-ups.

The thesis of this study is that both the local and the national government can affect the attraction for

firms to start up in a specific region by affecting the production factors in the area. For example, is there

access to educated people to hire? With help from Porters diamond model we can study the local

production factors and how they affect regional development and start-up firms.

In his book ”The Competitive Advantage of Nation” (1990) Porter describes how individual factors of

production together create a dynamic system. They contribute to countries’ comparative advantages

relative to other countries. (Porter, 1990)

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Porter’s model shows how local conditions and factors of production affect a country’s comparative

advantage. The local perspective makes the model fit for analysing the issue of this paper. It will be used

to create a framework for the choice of variables in the analysis to follow.

The model consists of four main parts that creates the environment where firms and entrepreneurs (i.e.

those likely to start a firm) operate:

Factor conditions

Demand conditions

Related and supporting industries

Firm strategy, structure and rivalry

Figure 1: Porters diamond, own production after Porter 1990

Factor conditions

Porter distinguishes between two different kinds of factors: general and specialized factors. He argues

that most countries have access to general factors as infrastructure and higher education. This means that

these general factors don’t have a big effect on countries’ comparative advantages since most have

similar general factors. They make out the base the specialized factors rest on. (Porter, 1990)

The specialized factors are what differ between countries. The specialized factors are for example

investment rate, production and quality requirements and how well the institutions involved are

administrated. (Porter, 1990)

Thus, it is fundamental how well the specialized factors are administered and used to exploit the general

factors that are available.

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”What is important for competitive advantage is unusually effective mechanism for creating and

upgrading factors that are advanced and specialized, such as a world-class research institute in

composite materials technology.” (Porter 1990 p 132)

One other aspect that differs between general and specialized factors is that they most likely are not

evenly spread over the country (Porter, 1990). This means that it becomes relevant to study how a

region can act to improve factor conditions. Since the general factors already are present it is up to every

region to use them as efficiently as possible.

Porter points out that domestic rivalry is what affect the establishment of factors of production the most.

Rivalry between local firms lead to an incentive to use existing factors as efficiently as possible, which

leads to increased corporation with for example research institutes and educational institutions. (Porter,

1990) It becomes important for the firms to acquire as good a position as possible at the local market

through effective use of the factors of production.

Demand conditions

Demand at the local market is an important competitive advantage. If the consumers at home are

discerning and trend-sensitive this means that the producers have to adjust and specialize their

production to meet the demand. Since the firms already meet these demands at a local level they are

ready for discerning consumers at other markets as well. (Nordlander, 2005) (Eklund, 2004)

How the local market function is decided by many different factors such as population, climate, norms

and what others industries there are. Porter stresses that the most important factor is local and domestic

rivalry. Through local rivalry a market evolves with consumers that are attentive and demanding. The

consumers learn to make demands and expect good service when more firms act in an area. (Porter,

1990)

Relating and supporting industries

The relating and supporting industries in the model are important parts of what Porter terms as a cluster.

A cluster consists of different parts that together create a favourable environment for production of a

specific service or commodity. Favourable conditions for cooperation with for example product

development or advertising is created. (Nordlander, 2005)

Relating and supporting industries also helps so that new businesses choose to locate in the area because

of the already existing cooperation they could gain from taking part in. Under this aspect we also have

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supporting institutions such as educational institutions, which is the main variable in this study. (Porter,

1990)

Firm strategy, structure and rivalry

As stated in the preceding points rivalry is of importance for the creation of comparative advantages. If

there are many firms which are active in an area the incentive for relating and supporting industries to

establish in the area is higher than if it’s only one firm. (Porter, 1990)

The creation of factors of production and effective use of already existing factors are positively affected

by a high level of rivalry. Moreover the local market becomes more demanding which is good for the

firms. One kind of supporting institution that the government can choose to invest in is universities and

other research institutes, and the possibility for this to be done is higher in an area that is bursting of

activity.

Summary

To sum up the theory section we can conclude that education and knowledge are important factors when

it comes to economic growth. Human capital can be included both in exogenous and endogenous growth

models, where the main difference is whether it has diminishing returns to scale or not. But we can see a

positive impact on economic growth in both models.

In this study formal education is chosen as the indicator for human capital and that investments in

educational institutions will lead to those positive effects seen in the theoretical discussion above. The

endogenous growth models tell us that the more you invest in human capital the more you get out of it.

Through Porter’s model we can see that there are any different aspects that influence whether firms want

to start up in a specific area or not. There are many different factors that the local government can

influence through political decisions, for example investments in education and raising the number of

skilled workers in the region.

Based on the theoretical discussion above, we can conclude that higher education and population growth

are relevant variables for the regression analysis that will be done below. They are both assumed to have

a positive impact on economic growth. On the basis of this the study will assume that a large share of

inhabitants with only a secondary education is negative in regards of number of start-ups for

municipalities. From Porter’s model we can understand that population and population density are

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important variables, together with the access to skilled labour through educational institution in or close

to the municipality.

4. Empirical Study

In this part of the paper there will be a review of the different variables as well as the differences

between the municipalities. The data used in this paper is secondary data from the years 2007 to 2010

obtained from Statistics Sweden, Swedish National Agency for Higher Education, Arbetsförmedlingen

and Tillväxtverket analysed at the municipal level.

4.1. Review of variables

4.1.1. Dependent variable

Number of start-ups per inhabitant (FIRM)

To obtain comparable data, the number of start-ups in each region is divided with the population. In

2010 a total of 66 681 new firms was launched in Sweden and in 2007 that number was 58,531 new

firms. In 2010 0.7 firms started per hundred inhabitants, and in 2007 0.64 firms started per hundred

inhabitants. The number of firms in total and the number of start-ups per hundred inhabitants have gone

up between 2007 and 2010.

4.1.2. Independent variables

Educational variables

Tertiary education (TED)

Based on the previous studies presented in this paper it is indicated that there are no clear answers to

what impact the proportion of university graduates has on the number of start-ups. Since the paper's

thesis is that the variable has a positive effect, this will be the expected outcome of the regression

analysis.

Secondary education (SED)

Based on the assumption that an increased proportions of population with tertiary education has a

positive effect on the number of start-ups; a higher proportion of the population with only secondary

20

education will have a negative impact on the number of start-ups. This variable will be expected to have

a negative impact in the regression analysis.

Dummy 1 (D1)

The dummy variable indicates whether the municipality has a university or is immediately adjacent to a

municipality that has one. With the thesis that a population with a higher level of education has a

positive effect on the number of start-up firms this variable is expected to have a positive effect on the

dependent variable.

Control variables

In a regression analysis there is always a risk for correlations that aren’t causal. It is possible that the

development in the variables FIRM and TED follow each other without actually influencing each other.

To avoid spuriosity a number of control variables are included in the regression analysis.

Population (POP)

Previous studies have shown that new firms benefit from establishing in metropolitan areas where there

is already a functioning infrastructure and a dissemination of knowledge sharing which they can take

advantage of. A large population is a hallmark of metropolitan regions. A large population also means

that there are many individuals who can start and run firms. The variable is expected to have a positive

impact on the number of start-up firms per capita.

Population Growth (POG)

The variable is the frequency of population growth divided by the total population. Previous studies

have not shown any consistent correlation between population growth and the number of start-ups, even

if the population growth leads to an increased demand. This variable is expected to have a positive

impact on the number of start-ups.

Population Density (POD)

The variable consists of data about the number of inhabitants per square kilometre in the municipalities.

Previous studies show that it is beneficial for businesses to establish in metropolitan areas and based on

this variable it is expected to have a positive impact on the number of start-ups.

21

Unemployment (UNEM)

Unemployment rate is computed by the average number of unemployed each month of each year

divided with the working population. More unemployed people means that the incentives to start up

your own business increase. Despite this the variable is expected to have a negative impact on the

number of start-ups. This because a high unemployment rate may indicate poor economic times, which

is not expected to increase the number of start-ups. If the prospects for profits seems negative the

incentives to start up a company decrease.

Mean income (INC)

An individual's access to capital and opportunities to take loans is often central to increasing

opportunities to invest and start their own business (Hansson 2008 page 7). This makes it easier for a

person with an upper middle class income to start their own business. An increased average income also

means an increased demand. Previous studies have failed to demonstrate any clear correlation between

an increase in average income and the number of start-ups. The part of the population that has a higher

income is often those with a college education and thus can get skilled jobs.

Demography (DEM)

The variable shows the proportion of the population that is of working age (20-64). The greater part of

the population that is of working age, the more people can start a business. The age group that starts the

most firms is 25-45 years (Calidoni et al., 2007). Moreover, the proportion of the population that is of

working age is often the group with most purchasing power, which could lead to increased demand in

the municipality. As previously noted, this does not mean that the number of new firms increase

automatically. In this paper the demographic variable is supposed to have a positive impact on the

number of start-ups.

The effect of unknown variables

The number of start-ups in a municipality is obviously affected by more variables than chosen in this

paper. The effects of these variables will be included in the model by the term ε.

4.2. Qualitative

Below is a qualitative analysis of what the different variable for the municipalities looked like in 2007

and 2010. During the years between 2007 and 2010 there was a financial crisis, in 2008, making it

relevant to study whether there are some clear differences in recession and boom.

22

All Swedish municipalities have been divided into 5 groups of 58 municipalities (290 in total), where

group 1 has the highest number of start-up firms per capita while Group 5 has the lowest number.

(Groups presented in the appendix) After grouping, certain patterns can be recognised concerning the

characteristics of the different groups.

Which industries have the highest number of start-ups?

There are substantial differences between industries regarding the number of start-ups. In the chart

below we see that the five categories stand out; construction, information and communication, trade,

business services as well as personal and cultural services. Many of the firms and careers that fit into

these categories are those where a college education is an important factor for success, for example in

information and communications technology.

The number of start-ups per industry, 2010

Table 1: The number of start-ups per industry, 2010 (Source: SCB 7 January 2013)

The municipalities with the highest and lowest number of start-ups per inhabitant

In 2010 an average of 0.78 firms started per hundred people in the 58 municipalities in group 1 and 0.36

firms per hundred persons in group 5. Figures show a similar pattern in 2007, but the proportion of start-

Health and social care

The education system

Credit institutions and insurance companies

Civil authorities and defense

Hotels and restaurants

Personal and cultural services, etc.

Business services

Trade

Property companies

Information and communication companies

Agriculture, forestry and fishing

Transport companies

Manufacturing and mining industry

Energy and environment companies

Construction

0 2000 4000 6000 8000 10000 12000 14000 16000 18000

23

ups is higher in both groups in relation to 2010. (0.86 in group 1 and 0.41 in group 5) This could be

related to the financial crisis that occurred in 2008 which caused a decline in the number of start-ups.

On the top-ten list of municipalities with the highest proportion of start-ups per capita, we find a

majority in the Stockholm area with the exceptions of Åre and Borgholm 2007 and Vellinge and

Härjedalen 2010. What distinguishes Åre, Borgholm and Härjedalen from other municipalities outside

the rural areas is that they are all tourism municipalities according to SKL:s definition. Vellinge is just

like the municipalities around Stockholm a suburb to a big city (Malmö). (SKL January 4, 2013)

After a review of the variables used in the regression analyses there are a few variables which show

clear differences between the first and the fifth group. They can be found below.

4.2.1. Unemployment

When it comes to unemployment, the mean value in 2010 was higher than in 2007 in the group with the

lowest proportion of start-ups. One reason for this may be that in the municipalities which have high

unemployment are characterised by low belief in the future. In this environment no one is eager to start

a business. Important to note is that the relationship is easily applied the other way around, a low

number of start-up leads to few new jobs, resulting in high unemployment. Worth noting is also that

there are significant differences within the groups, the maximum value of group 1 is 21.69 percent and

in group 5 is 22.76 percent. The difference between maximum and minimum value is significantly

greater in the group with the largest proportion of new companies (group 5), from 3.88 percent to 21.69.

4.2.2. Population growth

Regarding population growth, there is again a clear difference of mean values between the group with

the greatest proportion of start-ups and the group that has the least proportion of start-ups. The mean

value in Group 1 from 2010 was a population growth at 0.88, meaning a positive growth. In Group 5

from the same year, the mean value for population growth was negative at -0.35 percent. In Group 1, 12

of 58 municipalities have a negative population growth; while in Group 5 has 44 municipalities out of

58 with negative population growth.

This shows that it is not crucial for the number of start-ups wheter a municipality has negative

population growth or not but it is possible to discern a clear pattern. Even here there could be factors

such as belief in the future and optimism. If many who live in the municipality have a low belief in the

future and move away from there it probably affects what people think about the possibilities of starting

24

a business which can function and develop. If there is a negative population growth, it may in many

cases be that it is the well-educated population which moves and this reduces the chances of finding

qualified employees to your company.

4.2.3. A university in the own or neighbouring municipality

The importance of companies working closely with universities is mentioned in theories regarding

clusters. If we look at the groups in 2010 it becomes clear that the municipalities with a high percentage

of start-ups also have a university in the municipality or nearby. 6 of the 58 municipalities in Group 1

has at least one university while a large proportion of the remaining municipalities lie in close proximity

to these universities. Top 10 of the municipalities all have close access to educational institutions for

higher studies. In Group 5, it is however less; only 2 of the 58 municipalities have a university:

Trollhättan and Filipstad, and both of these are smaller universities. This group includes mostly smaller

municipalities where the average population is 18 249. Many of the municipalities are also far from

metropolitan areas in Sweden

4.2.4. Tertiary Education

When it comes to the proportion with a tertiary education there are clear differences between the group

with the largest proportion of start-ups and the group with the lowest percentage. At 2010 the average

in group 1 was 23.1 percent, i.e. about 23 per cent of the population have a tertiary education. In Group

5 average was lower: 14.99 percent.

Based on this an assumption can be made that there is a correlation between the percentage of residents

in a municipality with a tertiary education and the number of start-ups. This picture is also confirmed in

the images below. The diagrams show the average for each group, and the relationship between the

proportion of start-ups and the proportion of residents with a tertiary education. The premise of this

paper is that the more people who have a college education, the more companies started, this

relationship should be further investigated in the regression analysis.

In the municipalities that have a higher share of start-ups the relationship is stronger than in the

municipalities with a lower share of start-ups and highly educated inhabitants.

25

The number of start-ups related to the proportion of residents with a tertiary education 2010

Figure 2

The number of start-ups related to the proportion of residents with a tertiary education 2007

Figure 3

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

0,80

0,90

0,00

5,00

10,00

15,00

20,00

25,00

Eftergymnasialutbildning(procent)

Antal nystartadeföretag perhundra invånare

0,00

0,10

0,20

0,30

0,40

0,50

0,60

0,70

0,80

0,90

0,00

5,00

10,00

15,00

20,00

25,00

Grupp1

Grupp2

Grupp3

Grupp4

Grupp5

Eftergymnasialutbildning(procent)

Antal nystartadeföretag perhundra invånare

26

4.3. Quantitative part

Based on the variables described above, these data can now be analysed with help from a regression

analysis. This is to ensure the assumptions made, and to see which of the variables that have a

significant effect on the number of start-ups in Sweden's 290 municipalities. The regression analyse will

be done using a linear OLS model and cross-sectional data.

The regression model will show what factors have a significant affect the number of start-ups in the year

2007 to 2010. The results will be presented through four models for so that correlation between

variables can be detected. Then the results from the four models will be compared to see if some

variables have a significant impact those years.

The variables significance will be measured with help from a t-test, this will show us to what degree the

correlation between the variable and the dependent variable can be assumed to be significant. While the

t-test check the significance for each variable within the model the F-test will give us information about

the model as a whole. Through this we can compare the different models used. The F-test is a way to

test the model against a null hypothesis, the lower the number the more accurate is the model.

(Studendmund, 2011)

4.3.1. Explanation of variables

FIRM – The share of start-ups per capita

TED – Tertiary education

SED – Secondary education

D1 – 1 if there is a university in the municipality or the neighbouring municipalities

POP – Population

POG – Population growth

POD – Population density

UNEM – Unemployment

INC – Mean income

DEM – Demographics

27

Variables

Variable Description Source Expected impact

FIRM Number of start-ups Tillväxtanalys Dependent

variable

TED Tertiary education SCB +

SED Secondary education SCB -

D 1 Dummy 1 Högskoleverket +

POP Population SCB +

POG Population growth SCB +

POD Population density SCB +

UNEM Unemployment Arbetsförmedlingen -

INC Mean income SCB +/-

DEM Demographics SCB +

Table 2

4.4. Description of data

Variables Mean value Max value Min. value

FIRM 0,005495 0,011491 0,003015

TED 0,17144 0,450957 0,097989

SED 0,354881 0,430322 0,177957

POP 32065,11 820 443,3 2 506,25

POG 0,001298 0,027821 -0,01839

POD 133,8092 0,2 4362,675

UNEM 0,069585 0,14984 0,016892

INC 220,7596 429,375 185,725

DEM 0,6317 0,711618 0,557551

Table 3

5. Results

5.1. Regression models

1) FIRM = α + β1TED (10)

2) FRTG = α + β1TED + β2D1 + β3SED+ ε (11)

3) FRTG = α + β1TED + β2D1 + β3SED + β4POG + β5POD+ β8POP+ ε (12)

4) FRTG = α + β1TED + β2D1 + β3SED + β4POG + β5POD + β6POP +β7DEM+ β8UNEM+ β9INC+ ε (13)

28

5.2. Results

2007-2010 Model 1 Model 2 Model 3 Model 4

Independent

variables

Estimate Estimate Estimate Estimate

(standard deviation) (standard deviation) (standard deviation) (standard deviation)

Constant 0,00293459*** 0,00693939*** 0,00620919*** 0,00676264**

(0,000198565) (0,00143171) (0,0013345) (0,00283897)

TED 0,0149331*** 0,0113872*** 0,00628492*** 0,00713334***

(0,00109706) (0,00187835) (0,00190612) (1,24E-09)

D1

-0,000605796*** -0,000640925*** -0,000605014***

(0,000123441) (0,000114556) (0,000114872)

SED

-0,00861835*** -0,00446552 0,00115489

(0,00323601) (0,00308287) (0,00332586)

POG

0,0369881*** 0,0277724**

(0,0093178) (0,0116558)

POD

7,68267e-07*** 7,34072e-07***

(1,46E-07) (1,63E-07)

POP -3,56725e-010

(1,17057e-09)

1,34284e-09

(3,84987e-06)

UNEM

-0,0136917***

(0,00404855)

INC

2,00925E-06

(3,85E-06)

DEM

-0,00353172

(0,00398401)

N 290 290 290 290

R-squared 0,391488 0,44916 0,52984 0,556869

Adj R- squared 0,389375 0,443382 0,521563 0,544253

P-value (F) 6.43e-33 8.43e-37 1.16e-43 7.18e-45

F-value 185,2856 77,73575 64,01003 44,14054

Table 4

Standard deviation in parentheses

* Significant at the 10% level

** Significant at the 5% level

*** Significant at the 1% level

29

6. Analysis

6.1. Modell 1

Model 1 shows the relationship between the proportion of tertiary educated inhabitant and the number of

start-ups in the municipalities. When the proportion of tertiary educated increases by one unit the

number of start-ups increased by 1.4 per hundred inhabitants. The correlation is significant at the 1

percent level. Using the Adjusted R-square, we see that the proportion of inhabitants with tertiary

education could explain the number of start-ups to about 38 percent

The number of start-ups related to the proportion of the inhabitants with a tertiary education.

Figure 4

6.2. Modell 2

The model included two remaining educational variables (GYM and D1). These variables together

account for an explanatory power of 44 percent. All variables have significance at the 1 percent level.

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

0,45

0,5

0,003 0,004 0,005 0,006 0,007 0,008 0,009 0,01 0,011

EG

Y

FRTG

EGY versus FRTG (with least squares fit)

Y = 0,0274 + 26,2X

30

Secondary education has, as expected, a negative effect on the number of start-ups. Even the dummy

variable has a negative effect, which is not the expected outcome.

6.3. Modell 3

The third model is the first group of control variables are included. The variables of population,

population growth and population density leads to the explanatory power increases further to about 52

percent. Population growth and population density both have significance at the 1 percent level. The

population has no significant effect on the number of start-ups per 100 inhabitants.

6.4. Modell 4

The fourth and final model contains all variables and has an explanatory value of 54 per cent. Here the

variables unemployment, income and demographics are added. The variable unemployment is the only

one that has a significant effect on the number of start-ups per capita. When unemployment rise by one

unit this leads to that the number of start-ups decreases by 0.4 firms per hundred inhabitants. When the

variables are analysed one by one this will be in relation to model 4, since this model has the highest

value of Adjusted R-square, and can therefore be considered as the most accurate model.

6.5. Educational variables

Tertiary education

The proportion of the population with a tertiary education has a significant effect on the number of start-

ups in all models. This is in accordance with the expected outcome based on a number of previous

studies presented in this area. This result strengthens the studies mean that the proportion of university

graduates has a positive impact on the number of start-ups.

Secondary education

The proportion of the population with a secondary education only has a significant effect on the number

of start-ups in model two. This suggests that it is difficult to see what effect it has on the number of

start-ups. In model two the variable had a negative impact on the number of start-ups, as expected.

Dummy-variable D1

The regression analysis results show a negative relationship between having a college in the

31

municipality or in neighbouring municipality has a negative impact on the number of start-ups per 100

inhabitants.

One difficulty with this variable is that it includes all municipalities bordering a municipality with

college without taking into what account the communication looks like. If we would have chosen only

those municipalities that have a college in the municipality this group would become much too narrow

since many commutes between municipalities for study and work (Torége and others 2008 page 5). A

more accurate measure would be constructed by including all individuals living within a certain radius

of the college or has a particular route, but this was not possible to achieve within the framework of this

study.

Summary educational variables

In conclusion, as for many studies before neither this study received a clear answer about the

educational variables impact on the number of start-ups. The regression analyses show that the

proportion of university graduates in the municipality has a positive impact on the number of start-ups,

while the dummy variable has a negative impact.

From this we can see that the educational variables are of importance to the creation of new businesses.

The availability of university educated workers is clearly an important variable with a positive impact

on the number of start-ups, while the fact that the proximity of the universities provides a negative effect

means that the whole is indefinite. These inconsistent results are something that would be perfect to

investigate further in future studies.

6.6. Control variabels

The control variables that have a significant effect on the number of start-ups is population density,

population growth and unemployment.

The effect from the variable population density is consistent with the expected results and previous

studies. Previous studies have highlighted the benefits of establishing a firm in the urban regions to gain

access to for example dissemination of knowledge between companies. Population growth has a

significant positive impact on the number of start-ups at the 5 percent level. Previous studies have not

been able to come up with a clear answer on how population growth affects the number of start-ups.

This study indicates that the variable has a positive impact. Unemployment has as expected a negative

32

impact on the number of start-ups.

The rest of the variables

The population, average income and demographics do in this study not show any significant influence

on the number of start-ups.

7. Summary

Do a higher proportion of university graduates lead to an increased number of start-ups?

The purpose of this study was to look at whether a higher proportion of the population with tertiary

education leads to an increase in the number of start-ups per capita. The result presented above show

that an increased proportion of the population with college education leads to more firms. The short

answer to the question is yes.

Earlier studies in this field disagree about whether the proportion with higher education have a positive

impact or not. This study supports the results in those studies that has found a positive and significant

impact on the number of start-ups. A higher number of inhabitants with a tertiary education have a

positive impact on the number of start-ups. The results support the augmented Solow models that say

that human capital and population growth are important factors.

Porter’s model shows that by creating an environment where businesses can start up and developed, the

number of start-ups increases. Population density and population growth are two of the three control

variables that have significant effect suggests that clusters can have a positive impact on the number of

start-ups. That many people are in the same geographical area provides opportunities to create clusters.

That a municipality has a college or is next to a municipality that has a college has a negative impact on

the number of start-ups. This is in accordance with the theories that have concluded that higher

education has a negative effect on the number of start-ups. This leads to that theories of cluster

formation and the importance of universities to attract companies can neither be confirmed nor rejected

by this study. The results show the importance of further studies of the educational variable impact on

the number of start-ups per capita.

What factors influence the number of start-ups?

33

In conclusion, we note that the variables that have a significant effect on the number of start-ups (in

model 4) is tertiary education, whether the municipality has a college in or adjacent to the municipality,

population growth, population density and unemployment.

Based on the qualitative analysis it can be concluded that the municipalities that have a high number of

start-ups per capita as a group differ from the group with the lowest number of start-up companies per

capita.

It is both educational variables and other variables (here called control variables) that show a significant

effect on the number of start-ups. This is consistent with several previous studies that indicated that

there are many different factors that come into play.

34

8. References

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sverige-07.pdf

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36

Appendix

Skillnader mellan grupp 1 och 5

Tabell 1

Grupp 1 2010 Nya Företag Invånarantal Befolkningstillväxt Arbetslöshet Studier i annat

län

Medel 0,78 60017,14 0,88 10,35 0,37

Max 1,16 795163 2,89 21,69 3,97

Min 0,66 5406 -1,57 3,88 0 i 6 kommuner

Grupp 5 2010

Medel 0,36 15736,38 -0,35 16,09 0,34

Max 0,41 72090 2,44 22,76 3,5

Min 0,2 2549 -2,54 10,45 0 i 11 kommuner

Grupp 1 2010 Medelinkomst Medelålder Demografi Gymnasial utb Eftergymnasial

utb

Medel 233,86 41,06 57,48 32,65 21,64

Max 409,4 47,1 66,33 38,94 44,41

Min 183,2 36,1 51,87 17,99 11,07

Grupp 5 2010

Medel 202,38 43,71 55,07 36,55 13,69

Max 242,9 47,8 57,46 42,33 23,95

Min 176,7 38 48,21 32,03 9,69

37

Grupp 1 2007 Nya företag Invånarantal Befolkningstillväxt Arbetslöshet Studier i annat län

Medel 0,86 62406,17 0,77 9,07 0,41

Max 1,24 847073 2,5 17,61 4,35

Min 0,74 2736 -1,25 3,12 0 i 2 kommuner

Grupp 5 2007

Medel 0,41 18249,74 -0,42 12,91 0,4

Max 0,46 71641 1,18 28,71 3,21

Min 0,24 2460 -1,66 5,7 0 i 7 kommuner

Grupp 1 2007 Medelinkomst Medelålder Demografi Gymnasial utb Eftergymnasial utb

Medel 261,32 41,4 56,7 32,73 23,1

Max 449,9 47,8 66,3 40,49 45,9

Min 194,1 37 51,56 17,58 11,67

Grupp 5 2007

Medel 230,44 44,19 54,7 37,48 14,99

Max 274,9 47,8 57,97 43,74 20,84

Min 199,2 40,2 48,58 33,94 9,9